🚀🤖 Crawl4AI: the open-source web crawler for LLMs and AI agents

Latest: v0.9.4 (23 Sep 2026) · all releases →

Crawl4AI turns any website into clean, LLM-ready Markdown for RAG, AI agents and data pipelines. Run the open-source web crawler and scraper yourself, free forever, or use it hosted with one key: scrape, search and extract through one API, with MCP for your agent.

Two ways to use Crawl4AI

🐍 Run it yourself: open source, forever

pip install -U crawl4ai
crawl4ai-setup        # installs the browser, once

import asyncio
from crawl4ai import AsyncWebCrawler

async def main():
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(url="https://news.ycombinator.com")
        print(result.markdown)

asyncio.run(main())

Docker server, CLI and every option: Installation · docs.crawl4ai.com

☁️ Or use the cloud: no browsers, no proxies

Verify your email and your first $10 pack is on us (until 31 December 2026, then $5 to start). No card.

Get any page as Markdown:

curl -s https://api.crawl4ai.com/scrape \
  -H "Authorization: Bearer $CRAWL4AI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://news.ycombinator.com"}' | jq -r .markdown

The same key works for /search, /answer, /extract and many URLs at once (/scrape/batch, /scrape/jobs). Pay as you go: live prices.

Give it to your AI agent. Claude Code shown; Codex, Cursor and OpenCode →

claude mcp add --transport http crawl4ai https://api.crawl4ai.com/mcp \
  --header "Authorization: Bearer $CRAWL4AI_KEY"

Which one?

	🐍 Library	🐳 Your own server	☁️ Crawl4AI Cloud
Runs the browsers	you, in your Python process	you, in Docker on your machine	we do
JS-heavy pages and bot walls	your settings, your proxies	your settings, your proxies	handled for you, automatically
Web search	–	–	`/search` and `/answer`
Price	free, forever	free (your hosting)	pay as you go; your first $10 is on us

🤓 My Personal Story

I grew up on an Amstrad, thanks to my dad, and never stopped building. In grad school I specialized in NLP and built crawlers for research. That’s where I learned how much extraction matters.

In 2023, I needed web-to-Markdown. The “open source” option wanted an account, API token, and $16, and still under-delivered. I went turbo anger mode, built Crawl4AI in days, and it went viral. Now it’s the most-starred crawler on GitHub.

I made it open source for availability, anyone can use it without a gate. Now I’m building the platform for affordability, anyone can run serious crawls without breaking the bank. If that resonates, join in, send feedback, or just crawl something amazing.

That platform is live now: Crawl4AI Cloud.

Why developers pick Crawl4AI

LLM-ready output: smart Markdown with headings, tables, code and citation hints
Fast in practice: async browser pool, caching, minimal hops
Full control: sessions, proxies, cookies, user scripts, hooks
Adaptive intelligence: learns site patterns, explores only what matters
Deploy anywhere: no keys needed, CLI and Docker, or the hosted cloud

✨ Features

📝 Markdown generation

🧹 Clean Markdown: headings, lists, tables and code blocks, in a structure an LLM reads well.
🎯 Fit Markdown: filters remove menus, footers and boilerplate: PruningContentFilterLXML, BM25ContentFilter (for a query) and LLMContentFilter.
🔗 Citations: page links become a numbered reference list.
🛠️ Your own strategy: plug in a custom Markdown generator.

☁️ Same in the cloud: POST /scrape returns this Markdown, with no browser to run. Docs →

📊 Structured data extraction

🔎 CSS and XPath schemas: fast extraction with no LLM (JsonCssExtractionStrategy, JsonXPathExtractionStrategy, RegexExtractionStrategy).
🪄 Schema generator: describe what you want once; generate_schema writes a reusable schema.
🤖 LLM extraction: any LLM provider, open-source or hosted, into a typed JSON schema (LLMExtractionStrategy).
🧱 Chunking: topic, regex and sentence chunking for long pages.
🌌 Cosine similarity: find the chunks that match a query (CosineStrategy).

☁️ Same in the cloud: POST /extract, with no LLM key of your own. Docs →

🌐 Browser control

🖥️ Your own browser: persistent profiles with saved logins, cookies and settings.
🔄 Remote browsers: connect over the Chrome DevTools Protocol (CDP).
🔒 Sessions: keep a browser state across multi-step crawls.
🧩 Proxies: with authentication and rotation.
🕶️ Stealth mode: enable_stealth, and an undetected-browser adapter for sites that detect automation.
⚙️ Full control: headers, cookies, user agents, viewport.
🌍 Chromium, Firefox and WebKit.

🔎 Crawling and scraping

🕸️ Deep crawl: BFS, DFS and best-first strategies, with crash recovery (resume_state) for long crawls.
🧠 Adaptive crawling: AdaptiveCrawler stops when it has learned enough to answer your query.
🌱 URL discovery: AsyncUrlSeeder (sitemaps, Common Crawl) and DomainMapper; prefetch=True finds URLs 5 to 10 times faster.
🚀 Dynamic pages: run JavaScript, wait for elements, scroll the full page (scan_full_page) for infinite scroll and lazy images.
📸 Screenshots and PDFs of any page.
🖼️ Media and links: images, audio, video, srcset, internal and external links, iframes, metadata.
📂 Raw HTML and local files: raw: and file://.
🛠️ Hooks at every step of a crawl.
💾 Caching to skip repeated fetches.
⚡ Many URLs at once: arun_many with a memory-adaptive dispatcher.

☁️ Same in the cloud: up to 50 URLs in one streamed call, or 10,000 in a background job. Docs →

🐳 Self-hosting (Docker)

🔐 Secure by default: every endpoint needs your CRAWL4AI_API_TOKEN.
🧰 REST API: /md, /html, /crawl, /crawl/stream, /screenshot, /pdf, /execute_js.
🤖 MCP: connect Claude Code and other agents to your own server.
📊 Monitoring dashboard and playground, a browser pool with pre-warmed pages.
🏗️ AMD64 and ARM64 images.

☁️ Rather not run a server? The cloud is the same idea, hosted. Get a key →

☁️ What the cloud adds

🔍 Web search API: GET /search, browser-free, ranked and cleaned. Docs →
💬 Answers: GET /answer gives a direct answer to a question (experimental). Docs →
🧪 Extraction without your own LLM key: POST /extract. Docs →
🧗 JS-heavy pages and bot walls: handled automatically; you never pick an engine. Docs →
🤝 MCP for your agent: one line in Claude Code, Codex, Cursor or OpenCode. Docs →

🛠️ Installation

🐍 pip

pip install -U crawl4ai
crawl4ai-setup      # installs and sets up the browser
crawl4ai-doctor     # checks the installation

If the browser setup fails, install it by hand:

python -m playwright install --with-deps chromium

Pre-release versions: pip install crawl4ai --pre

Development install, for contributors:

git clone https://github.com/unclecode/crawl4ai.git
cd crawl4ai
pip install -e ".[all]"     # or: pip install -e .   (the core only)

🐳 Docker server

The server needs a token. Without one it answers only inside its container.

export CRAWL4AI_API_TOKEN="$(openssl rand -hex 32)"
docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g \
  -e CRAWL4AI_API_TOKEN="$CRAWL4AI_API_TOKEN" \
  unclecode/crawl4ai:latest

Test it (allow about 10 seconds for the start):

curl -s http://localhost:11235/md \
  -H "Authorization: Bearer $CRAWL4AI_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://news.ycombinator.com"}' | jq -r .markdown

The dashboard is at http://localhost:11235/dashboard, the playground at http://localhost:11235/playground. LLM keys, MCP and every setting: Self-hosting guide.

⌨️ Command line (`crwl`)

# A page as Markdown
crwl https://news.ycombinator.com -o markdown

# Deep crawl, breadth first, at most 10 pages
crwl https://docs.crawl4ai.com --deep-crawl bfs --max-pages 10

# Ask a question about a page (needs an LLM key: crwl config)
crwl https://www.example.com/products -q "Extract all product prices"

🔬 Advanced usage examples

More in docs/examples.

📝 Clean and fit Markdown

import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
from crawl4ai.content_filter_strategy import PruningContentFilterLXML
from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator

async def main():
    run_config = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS,
        markdown_generator=DefaultMarkdownGenerator(
            content_filter=PruningContentFilterLXML(threshold=0.48, threshold_type="fixed", min_word_threshold=0)
        ),
    )
    async with AsyncWebCrawler(config=BrowserConfig(headless=True)) as crawler:
        result = await crawler.arun(url="https://en.wikipedia.org/wiki/Web_crawler", config=run_config)
        print(len(result.markdown.raw_markdown), "characters of raw Markdown")
        print(len(result.markdown.fit_markdown), "characters after the filter")

asyncio.run(main())

🖥️ A JavaScript page and structured data, without an LLM

import asyncio, json
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, JsonCssExtractionStrategy

schema = {
    "name": "Quotes",
    "baseSelector": "div.quote",
    "fields": [
        {"name": "text", "selector": "span.text", "type": "text"},
        {"name": "author", "selector": "small.author", "type": "text"},
        {"name": "tags", "selector": "a.tag", "type": "list", "fields": [{"name": "tag", "type": "text"}]},
    ],
}

async def main():
    run_config = CrawlerRunConfig(
        extraction_strategy=JsonCssExtractionStrategy(schema),
        scan_full_page=True,   # scroll to the end, so the page loads every quote
        scroll_delay=0.5,
        cache_mode=CacheMode.BYPASS,
    )
    async with AsyncWebCrawler(config=BrowserConfig(headless=True)) as crawler:
        result = await crawler.arun(url="https://quotes.toscrape.com/scroll", config=run_config)
        quotes = json.loads(result.extracted_content)
        print(f"Extracted {len(quotes)} quotes")
        print(json.dumps(quotes[0], indent=2))

asyncio.run(main())

📚 Structured data with an LLM

import os, asyncio
from pydantic import BaseModel, Field
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, CacheMode, LLMConfig, LLMExtractionStrategy

class ModelFee(BaseModel):
    model_name: str = Field(..., description="Name of the model.")
    input_fee: str = Field(..., description="Fee for input tokens.")
    output_fee: str = Field(..., description="Fee for output tokens.")

async def main():
    run_config = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS,
        extraction_strategy=LLMExtractionStrategy(
            # any provider LiteLLM supports, e.g. "ollama/llama3.3" with api_token="no-token"
            llm_config=LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY")),
            schema=ModelFee.model_json_schema(),
            extraction_type="schema",
            instruction="Extract every model name with its input and output token fee.",
        ),
    )
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(url="https://openai.com/api/pricing/", config=run_config)
        print(result.extracted_content)

asyncio.run(main())

🤖 Your own browser with a saved profile

import os, asyncio
from pathlib import Path
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode

async def main():
    user_data_dir = os.path.join(Path.home(), ".crawl4ai", "browser_profile")
    os.makedirs(user_data_dir, exist_ok=True)
    browser_config = BrowserConfig(headless=True, user_data_dir=user_data_dir, use_persistent_context=True)
    run_config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS, magic=True)
    async with AsyncWebCrawler(config=browser_config) as crawler:
        result = await crawler.arun(url="ADDRESS_OF_A_CHALLENGING_WEBSITE", config=run_config)
        print(result.success, len(result.markdown))

asyncio.run(main())

📖 Documentation

Library docs, guides and API reference: docs.crawl4ai.com
Cloud docs: crawl4ai.com/docs
Release notes: releases · Roadmap: ROADMAP.md

🤝 Contributing

We welcome contributions from the open-source community. Check out our contribution guidelines for more information.

📄 License & Attribution

This project is licensed under the Apache License 2.0, attribution is recommended via the badges below. See the Apache 2.0 License file for details.

Attribution Requirements

When using Crawl4AI, you must include one of the following attribution methods:

📈 1. Badge Attribution (Recommended)

Add one of these badges to your README, documentation, or website:

Theme	Badge
Disco Theme (Animated)
Night Theme (Dark with Neon)
Dark Theme (Classic)
Light Theme (Classic)

HTML code for adding the badges:

<!-- Disco Theme (Animated) -->
<a href="https://github.com/unclecode/crawl4ai">
  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-disco.svg" alt="Powered by Crawl4AI" width="200"/>
</a>

<!-- Night Theme (Dark with Neon) -->
<a href="https://github.com/unclecode/crawl4ai">
  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-night.svg" alt="Powered by Crawl4AI" width="200"/>
</a>

<!-- Dark Theme (Classic) -->
<a href="https://github.com/unclecode/crawl4ai">
  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-dark.svg" alt="Powered by Crawl4AI" width="200"/>
</a>

<!-- Light Theme (Classic) -->
<a href="https://github.com/unclecode/crawl4ai">
  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-light.svg" alt="Powered by Crawl4AI" width="200"/>
</a>

<!-- Simple Shield Badge -->
<a href="https://github.com/unclecode/crawl4ai">
  <img src="https://img.shields.io/badge/Powered%20by-Crawl4AI-blue?style=flat-square" alt="Powered by Crawl4AI"/>
</a>

📖 2. Text Attribution

Add this line to your documentation: ``` This project uses Crawl4AI (https://github.com/unclecode/crawl4ai) for web data extraction. ```

crawl4ai